Understanding the cognitive structure of explanations— andthe cognitive processes that assemble them— is a milestonefor understanding how people learn and communicate. Re-cent research on explanatory coexistence suggests that peo-ple’s causal beliefs are less globally coherent than previouslythought: people use seemingly-competing supernatural and bi-ological causes to explain different aspects of the same phe-nomenon, or they assemble supernatural and biological causesinto single, coherent explanations (Legare & Gelman, 2008;Legare & Shtulman, 2018; Shtulman & Lombrozo, 2016).This coexistence— and unexpected coherence— of diversecausal mechanisms poses interesting questions about the roleof coherence and fragmentation in people’s mental models andexplanations. This paper presents a computational model ofexplanatory coherence in the well-characterized domain of dis-ease transmission, extending a previous cognitive model ofexplanation-based conceptual change (Friedman, Forbus, &Sherin, 2018). Our approach (1) retrieves diverse causal modelfragments based on the phenomenon to explain, (2) assem-bles coherent causal models using relevance-directed abduc-tive reasoning, and (3) selects explanatory paths that supportwithin-explanation and within-scenario coherence. Our modelsimulates the three different types of explanatory coexistencedetailed in the literature.

Moral disengagement is a mechanism whereby people distance or disconnect their actions from their moral evaluation. This work presents a novel knowledge graph schema, dataset, and transformer-based NLP model to identify and represent indicators of moral disengagement in text. Our graph schema is informed by Albert Bandura’s psychosocial mechanisms of moral disengagement, including dehumanization, victimization, moral condemnation and justification, and attribution (or displacement) of responsibility. Our preliminary dataset is comprised of online posts from five different communities. We present initial evidence that (1) our theory-based schema can represent moral disengagement indicators across these communities and (2) our transformer-based NLP model can identify indicators of moral disengagement in text. As it matures, this thread of computational social science research can help us understand the spread of morally-disengaged language and its effect on online communities.

Understanding the values, norms, behaviors, and causal beliefs of communities is a central goal of cognitive science, with practical benefits of grasping and improving community factors such as healthcare delivery. These cultural causal beliefs are evident, in part, within narratives, interview transcripts, ethnography, and other textual sources, but analyzing these texts presently involves tedious expert hand-coding or relatively shallow qualitative text analysis or classification. We present a novel approach for extracting graphical causal models from text via NLP, including qualitative causality, intentions, teleology, sentiment, welfare, social influence, and other rationale. The factors (i.e., nodes) of these causal models are tagged with ethnographic attributes and word-senses, allowing aggregation of causal models over thousands of passages to identify correlations and recurring themes. We apply this approach to a corpus of narrative interviews about maternal and child health and healthcare delivery in Bihar, India, corroborating the hand-coded results of human experts and also identifying novel insights about explanatory structure.